scholarly journals Transvection regulates the sex-biased expression of a fly X-linked gene

Science ◽  
2021 ◽  
Vol 371 (6527) ◽  
pp. 396-400 ◽  
Author(s):  
Charalampos Chrysovalantis Galouzis ◽  
Benjamin Prud’homme
Keyword(s):  
Gene Expression ◽  
Sexual Dimorphism ◽  
Sex Determination ◽  
Expression Pattern ◽  
X Chromosome ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Regulatory Interaction ◽  
Linked Gene ◽  
Homologous Chromosomes

Sexual dimorphism in animals results from sex-biased gene expression patterns. These patterns are controlled by genetic sex determination hierarchies that establish the sex of an individual. Here we show that the male-biased wing expression pattern of the Drosophila biarmipes gene yellow, located on the X chromosome, is independent of the fly sex determination hierarchy. Instead, we find that a regulatory interaction between yellow alleles on homologous chromosomes (a process known as transvection) silences the activity of a yellow enhancer functioning in the wing. Therefore, this enhancer can be active in males (XY) but not in females (XX). This transvection-dependent enhancer silencing requires the yellow intron and the chromatin architecture protein Mod(mdg4). Our results suggest that transvection can contribute more generally to the sex-biased expression of X-linked genes.

Biological Image Analysis via Matrix Approximation

2011 ◽  
pp. 166-170
Author(s):  
Jieping Ye ◽  
Ravi Janardan ◽  
Sudhir Kumar
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Controlled Vocabulary ◽  
Gene Expression Patterns ◽  
Image Content ◽  
Gene Expressions ◽  
Microarray Gene Expression ◽  
Individual Gene

Understanding the roles of genes and their interactions is one of the central challenges in genome research. One popular approach is based on the analysis of microarray gene expression data (Golub et al., 1999; White, et al., 1999; Oshlack et al., 2007). By their very nature, these data often do not capture spatial patterns of individual gene expressions, which is accomplished by direct visualization of the presence or absence of gene products (mRNA or protein) (e.g., Tomancak et al., 2002; Christiansen et al., 2006). For instance, the gene expression pattern images of a Drosophila melanogaster embryo capture the spatial and temporal distribution of gene expression patterns at a given developmental stage (Bownes, 1975; Tsai et al., 1998; Myasnikova et al., 2002; Harmon et al., 2007). The identification of genes showing spatial overlaps in their expression patterns is fundamentally important to formulating and testing gene interaction hypotheses (Kumar et al., 2002; Tomancak et al., 2002; Gurunathan et al., 2004; Peng & Myers, 2004; Pan et al., 2006). Recent high-throughput experiments of Drosophila have produced over fifty thousand images (http://www. fruitfly.org/cgi-bin/ex/insitu.pl). It is thus desirable to design efficient computational approaches that can automatically retrieve images with overlapping expression patterns. There are two primary ways of accomplishing this task. In one approach, gene expression patterns are described using a controlled vocabulary, and images containing overlapping patterns are found based on the similarity of textual annotations. In the second approach, the most similar expression patterns are identified by a direct comparison of image content, emulating the visual inspection carried out by biologists [(Kumar et al., 2002); see also www.flyexpress.net]. The direct comparison of image content is expected to be complementary to, and more powerful than, the controlled vocabulary approach, because it is unlikely that all attributes of an expression pattern can be completely captured via textual descriptions. Hence, to facilitate the efficient and widespread use of such datasets, there is a significant need for sophisticated, high-performance, informatics-based solutions for the analysis of large collections of biological images.

scholarly journals BEST: A Novel Computational Approach for Comparing Gene Expression Patterns From Early Stages of Drosophila melanogaster Development

Genetics ◽  
2002 ◽  
Vol 162 (4) ◽  
pp. 2037-2047
Author(s):  
Sudhir Kumar ◽  
Karthik Jayaraman ◽  
Sethuraman Panchanathan ◽  
Rajalakshmi Gurunathan ◽  
Ana Marti-Subirana ◽  
...  
Keyword(s):  
Gene Expression ◽  
Developmental Biology ◽  
Drosophila Melanogaster ◽  
Expression Pattern ◽  
Early Stage ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Computational Approach ◽  
Gene Expression Patterns ◽  
Early Stages

Abstract Embryonic gene expression patterns are an indispensable part of modern developmental biology. Currently, investigators must visually inspect numerous images containing embryonic expression patterns to identify spatially similar patterns for inferring potential genetic interactions. The lack of a computational approach to identify pattern similarities is an impediment to advancement in developmental biology research because of the rapidly increasing amount of available embryonic gene expression data. Therefore, we have developed computational approaches to automate the comparison of gene expression patterns contained in images of early stage Drosophila melanogaster embryos (prior to the beginning of germ-band elongation); similarities and differences in gene expression patterns in these early stages have extensive developmental effects. Here we describe a basic expression search tool (BEST) to retrieve best matching expression patterns for a given query expression pattern and a computational device for gene interaction inference using gene expression pattern images and information on the associated genotypes and probes. Analysis of a prototype collection of Drosophila gene expression pattern images is presented to demonstrate the utility of these methods in identifying biologically meaningful matches and inferring gene interactions by direct image content analysis. In particular, the use of BEST searches for gene expression patterns is akin to that of BLAST searches for finding similar sequences. These computational developmental biology methodologies are likely to make the great wealth of embryonic gene expression pattern data easily accessible and to accelerate the discovery of developmental networks.

scholarly journals In Silico Prediction of Transcription Factor Collaborations Underlying Phenotypic Sexual Dimorphism in Zebrafish (Danio rerio)

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 873
Author(s):  
Shahrbanou Hosseini ◽  
Armin Otto Schmitt ◽  
Jens Tetens ◽  
Bertram Brenig ◽  
Henner Simianer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Sexual Dimorphism ◽  
Sex Determination ◽  
Expression Patterns ◽  
Regulation Of Gene Expression ◽  
Colour Pattern ◽  
Promoter Regions ◽  
Dimorphic Gene Expression

The transcriptional regulation of gene expression in higher organisms is essential for different cellular and biological processes. These processes are controlled by transcription factors and their combinatorial interplay, which are crucial for complex genetic programs and transcriptional machinery. The regulation of sex-biased gene expression plays a major role in phenotypic sexual dimorphism in many species, causing dimorphic gene expression patterns between two different sexes. The role of transcription factor (TF) in gene regulatory mechanisms so far has not been studied for sex determination and sex-associated colour patterning in zebrafish with respect to phenotypic sexual dimorphism. To address this open biological issue, we applied bioinformatics approaches for identifying the predicted TF pairs based on their binding sites for sex and colour genes in zebrafish. In this study, we identified 25 (e.g., STAT6-GATA4; JUN-GATA4; SOX9-JUN) and 14 (e.g., IRF-STAT6; SOX9-JUN; STAT6-GATA4) potentially cooperating TFs based on their binding patterns in promoter regions for sex determination and colour pattern genes in zebrafish, respectively. The comparison between identified TFs for sex and colour genes revealed several predicted TF pairs (e.g., STAT6-GATA4; JUN-SOX9) are common for both phenotypes, which may play a pivotal role in phenotypic sexual dimorphism in zebrafish.

scholarly journals In Silico Analysis of the Gene Expression Patterns between Aldosterone-Producing Adenoma and Nonfunctional Adrenocortical Adenoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yongfa Dai ◽  
Jing Li ◽  
Hong Wen ◽  
Jie Liu ◽  
Jianling Li
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Primary Aldosteronism ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
Pathophysiological Mechanism ◽  
Adrenocortical Adenoma ◽  
Hub Genes ◽  
Aldosterone Producing Adenoma

Primary aldosteronism is the most common form of secondary hypertension, and aldosteronoma makes up a significant proportion of primary aldosteronism cases. Aldosteronoma is also called aldosterone-producing adenoma (APA). Although there have been many studies about APA, the pathogenesis of this disease is not yet fully understood. In this study, we aimed to find out the difference of gene expression patterns between APA and nonfunctional adrenocortical adenoma (NFAA) using a weighted gene coexpression network (WGCNA) and differentially expressed gene (DEG) analysis; only the genes that meet the corresponding standards of both methods were defined as real hub genes and then used for further analysis. Twenty-nine real hub genes were found out, most of which were enriched in the phospholipid metabolic process. WISP2, S100A10, SSTR5-AS1, SLC29A1, APOC1, and SLITRK4 are six real hub genes with the same gene expression pattern between the combined and validation datasets, three of which indirectly or directly participate in lipid metabolism including WISP2, S100A10, and APOC1. According to the gene expression pattern of DEGs, we speculated five candidate drugs with potential therapeutic value for APA, one of which is cycloheximide, an inhibitor for phospholipid biosynthesis. All the evidence suggests that phospholipid metabolism may be an important pathophysiological mechanism for APA. Our study provides a new perspective regarding the pathophysiological mechanism of APA and offers some small molecules that may possibly be effective drugs against APA.

scholarly journals X chromosome duplications affect a region of the chromosome they do not duplicate in Caenorhabditis elegans.

Genetics ◽  
1988 ◽  
Vol 119 (2) ◽  
pp. 365-375
Author(s):  
P M Meneely ◽  
K D Nordstrom
Keyword(s):  
Gene Expression ◽  
Caenorhabditis Elegans ◽  
Sex Determination ◽  
X Chromosome ◽  
Dosage Compensation ◽  
Linked Gene ◽  
Hypomorphic Mutation ◽  
Level Of Activity

Abstract X chromosome duplications have been used previously to vary the dose of specific regions of the X chromosome to study dosage compensation and sex determination in Caenorhabditis elegans. We show here that duplications suppress and X-linked hypomorphic mutation and elevate the level of activity of an X-linked enzyme, although these two genes are located in a region of the X chromosome that is not duplicated. The effects do not depend on the region of the X chromosome duplicated and is stronger in strains with two doses of a duplication than in strains with one dose. This is evidence for a general elevation of X-linked gene expression in strains carrying X-chromosome duplications, consistent with the hypothesis that the duplications titrate a repressor acting on many X-linked genes.

scholarly journals Sexual Dimorphism in Growth Rate and Gene Expression Throughout Immature Development in Wild Type Chrysomya rufifacies (Diptera: Calliphoridae) Macquart

2021 ◽  
Vol 9 ◽  
Author(s):  
Meaghan L. Pimsler ◽  
Carl E. Hjelmen ◽  
Michelle M. Jonika ◽  
Anika Sharma ◽  
Shuhua Fu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sexual Dimorphism ◽  
Sex Determination ◽  
Splice Variants ◽  
Expression Patterns ◽  
Relevant Information ◽  
Wild Type ◽  
Chrysomya Rufifacies ◽  
Immature Development ◽  
Species Specific

Reliability of forensic entomology analyses to produce relevant information to a given case requires an understanding of the underlying arthropod population(s) of interest and the factors contributing to variability. Common traits for analyses are affected by a variety of genetic and environmental factors. One trait of interest in forensic investigations has been species-specific temperature-dependent growth rates. Recent work indicates sexual dimorphism may be important in the analysis of such traits and related genetic markers of age. However, studying sexual dimorphic patterns of gene expression throughout immature development in wild-type insects can be difficult due to a lack of genetic tools, and the limits of most sex-determination mechanisms. Chrysomya rufifacies, however, is a particularly tractable system to address these issues as it has a monogenic sex determination system, meaning females have only a single-sex of offspring throughout their life. Using modified breeding procedures (to ensure single-female egg clutches) and transcriptomics, we investigated sexual dimorphism in development rate and gene expression. Females develop slower than males (9 h difference from egg to eclosion respectively) even at 30°C, with an average egg-to-eclosion time of 225 h for males and 234 h for females. Given that many key genes rely on sex-specific splicing for the development and maintenance of sexually dimorphic traits, we used a transcriptomic approach to identify different expression of gene splice variants. We find that 98.4% of assembled nodes exhibited sex-specific, stage-specific, to sex-by-stage specific patterns of expression. However, the greatest signal in the expression data is differentiation by developmental stage, indicating that sexual dimorphism in gene expression during development may not be investigatively important and that markers of age may be relatively independent of sex. Subtle differences in these gene expression patterns can be detected as early as 4 h post-oviposition, and 12 of these nodes demonstrate homology with key Drosophila sex determination genes, providing clues regarding the distinct sex determination mechanism of C. rufifacies. Finally, we validated the transcriptome analyses through qPCR and have identified five genes that are developmentally informative within and between sexes.

Zfy gene expression patterns are not compatible with a primary role in mouse sex determination

Nature ◽  
1989 ◽  
Vol 342 (6252) ◽  
pp. 940-942 ◽  
Author(s):  
Peter Koopman ◽  
John Gubbay ◽  
Jérôme Collignon ◽  
Robin Lovell-Badge
Keyword(s):  
Gene Expression ◽  
Sex Determination ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Primary Role

AML with Translocation T(8;16) Shows Unique Cytomorphological, Cytogenetic, Molecular, and Prognostic Features and Therefore Qualifies as An Own Entity According to WHO Criteria.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1197-1197
Author(s):  
Alexander Kohlmann ◽  
Martin Dugas ◽  
Hans-Ulrich Klein ◽  
Christian Ruckert ◽  
Wolfgang Kern ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
De Novo ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
Specific Gene ◽  
Differentiation Potential ◽  
Who Criteria ◽  
Prognostic Features

Abstract Balanced chromosomal rearrangements define distinct biological subsets in acute myeloid leukemia (AML). It is recognized that recurrent balanced aberrations, such as t(15;17), t(8;21), inv(16), and 11q23/MLL translocations, show a close correlation to cytomorphology and also harbor specific gene expression signatures. We here present a cohort of 13 AML cases with t(8;16)(p11;p13). This translocation is rare with only 13 cases (6 males, 7 females) diagnosed from our overall cohort of 6124 cases of AML over recent years, and is more frequently found in therapy-related AML than in de novo AML (7/438 t-AML, and 6/5686 de novo, p=0.00001). Prognosis was poor with median overall survival of 4.7 months. Five patients deceased within the first month after diagnosis. AML with t(8;16) is characterized by striking cytomorphologic features: In all 13 cases the positivity for myeloperoxidase (MPO) on bone marrow smears was >30% (median: 85%) and intriguingly, in parallel also >40% (median: 88%) of blast cells stained strongly positive for non-specific esterase (NSE) in the same cell, suggesting that AML with t(8;16) arise from a very early stem cell with both myeloid and monoblastic differentiation potential. Therefore, AML with t(8;16) cases can not be classified according to standard FAB categories. Morphologically we also detected erythrophagocytosis in 7/13 cases, a specific feature in AML with t(8;16) that was previously described. With respect to cytogenetics, 6/13 patients had t(8;16)(p11;p13) as sole abnormality. 7/13 patients demonstrated additional non-recurrent abnormalities, 4 cases with single additional aberrations, and 3 cases with two or more additional aberrations. Molecular analyses detected the MYST3- CREBBP fusion transcript in all cases tested (12/12). We then compared gene expression patterns in 7 cases of AML with t(8;16) to: (i) AML FAB subtypes M1 and M4/5 with strong MPO or NSE with normal karyotype and to (ii) distinct AML subtypes with balanced chromosomal aberrations according to WHO classification. In a first series using Affymetrix HG-U133A+B microarrays 4 cases of AML with t(8;16) were compared to FAB M1 (n=46), M4 (n=41), M5a (n=9), and M5b (n=16). Hierarchical clustering and principal component analyses revealed that AML with t(8;16) were intercalating rather with FAB subtypes M4 and M5b and did not cluster near to FAB M1, although strong positivity for MPO was seen in all t(8;16) cases. Thus, monocytic characteristics influence the gene expression pattern stronger than myeloid features. When further compared to AML WHO subtypes t(15;17) (n=43), t(8;21) (n=43), inv(16) (n=49), and 11q23/MLL (n=50), AML with t(8;16) samples were repeatedly grouped in the vicinity of the 11q23/MLL cases. This can be explained by a similar expression of genes such as EAF2, HOXA9, HOXA10, PRKCD, or HNMT. Yet, in a subsequent pairwise comparison AML with t(8;16) could also be clearly discriminated from 11q23/MLL with differentially expressed genes including CAPRIN1, RAN, SMARCD2, LRRC41, or H2BFS, higher expressed in AML with t(8;16) and SOCS2, PRAME, RUNX3, or TPT1, lower expressed in AML with t(8;16), respectively. Moreover, the respective FAB-type or WHO-type signatures were validated on a separate cohort of patients (n=3 AML with t(8;16); n=107 other AML subtypes as above), all prospectively analyzed with the successor HG-U133 Plus 2.0 microarray. Again, in direct comparison to FAB-type or WHO-type cases, dominant and unique gene expression patterns were seen for AML with t(8;16), confirming the molecular distinctiveness of this rare AML entity. Using a classification algorithm we were able to correctly predict all AML with t(8;16) cases by their gene expression pattern. This accuracy was observed not only for both FAB-type and WHO-type signatures, but also correctly classified the cases across the different patient cohorts and microarray designs. In conclusion, AML with t(8;16) is a specific subtype of AML with very poor prognosis that often presents as treatment-related AML and with particular characteristics not only in morphology and clinical profile, but also on a molecular level. Due to these unique features, it qualifies as a specific recurrent entity according to WHO criteria.

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